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Record W3196330023 · doi:10.1115/1.4052275

A Surface Energy Approach to Developing an Analytical Model for the Underfill Flow Process in Flip-Chip Packaging

2021· article· en· W3196330023 on OpenAlexaff
Xingjun Yao, Weijie Jiang, Jiahui Yang, Junjie Fang, Wenjun Zhang

Bibliographic record

VenueJournal of Electronic Packaging · 2021
Typearticle
Languageen
FieldEngineering
TopicElectronic Packaging and Soldering Technologies
Canadian institutionsUniversity of Saskatchewan
FundersNatural Science Foundation of Shanghai
KeywordsFlip chipSolderingMechanical engineeringFlow (mathematics)ChipProcess (computing)Electronic packagingMaterials scienceIntegrated circuit packagingThermal copper pillar bumpElectronic engineeringComputer scienceEngineeringMechanicsElectrical engineeringComposite material

Abstract

fetched live from OpenAlex

Abstract This paper presents a new approach to formulating an analytical model for the underfill process in flip-chip packaging to predict the flow front and the filling time. The new approach is based on the concept of surface energy along with the energy conservation principle. This approach avoids the need of modeling the flow path to predict the flow front and the filling time, and thus it is suitable to different configurations of solder bumps, including different shapes and arrangements of solder bumps in flip-chip packaging. An experiment along with the computational fluid dynamics simulation was performed based on a proprietarily developed testbed to verify the effectiveness of this approach. Both the experimental and simulation results show that the proposed approach along with its model is accurate for flip-chip packages with different configurations besides the configuration of a regular triangle arrangement of solder bumps and a spherical shape of the solder bump.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.280
Teacher spread0.246 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

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